In relation extraction tasks， distant supervision is a common method for automatic data labeling. However， this method will introduce a large amount of noisy data， which affects the performance of the model. In order to solve the problem of noisy data， a relation extraction method based on negative training and transfer learning was proposed. Firstly， a noisy data recognition model was trained through negative training method. Then， the noisy data were filtered and relabeled according to the predicted probability value of the sample， Finally， a transfer learning method was used to solve the domain shift problem existing in distant supervision tasks， and the precision and recall of the model were further improved. Based on Thangka culture， a relation extraction dataset with national characteristics was constructed. Experimental results show that the F1 score of the proposed method reaches 91.67%， which is 3.95 percentage points higher than that of SENT （Sentence level distant relation Extraction via Negative Training） method， and is much higher than those of the relation extraction methods based on BERT （Bidirectional Encoder Representations from Transformers）， BiLSTM+ATT（Bi-directional Long Short-Term Memory and Attention）， and PCNN （Piecewise Convolutional Neural Network）.

For the problems of long runtime, ignoring the difference between classes of sample, the paper put forward an algorithm called Global Weighted Sparse Locality Preserving Projection (GWSLPP) based on Sparse Preserving Projection (SPP). The algorithm made sample have good identification ability while maintaining the sparse reconstruction relations of the samples. The algorithm processed the samples though sparse reconstruction, then made the sample on the projection and maximized the divergence between classes of sample. It got the projection and classified the sample at last. The algorithm made the experiments on FERET face database and YALE face database. The experimental results show the GWSLPP algorithm is superior to the Locality Preserving Projection (LPP), SPP and FisherFace algorithm in both execution time and recognition rate. The execution time is only 25s and the recognition rate can reach more than 95%. The experimental data prove the effectiveness of the algorithm.